Improving Solar Flare Prediction by Time Series Outlier Detection
- URL: http://arxiv.org/abs/2206.07197v1
- Date: Tue, 14 Jun 2022 22:54:39 GMT
- Title: Improving Solar Flare Prediction by Time Series Outlier Detection
- Authors: Junzhi Wen, Md Reazul Islam, Azim Ahmadzadeh, Rafal A. Angryk
- Abstract summary: outliers on the reliability and those models' performance.
We employ Isolation Forest to detect the outliers among the weaker flare instances.
We achieve a 279% increase in True Skill Statistic and 68% increase in Heidke Skill Score.
- Score: 1.0131895986034316
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Solar flares not only pose risks to outer space technologies and astronauts'
well being, but also cause disruptions on earth to our hight-tech,
interconnected infrastructure our lives highly depend on. While a number of
machine-learning methods have been proposed to improve flare prediction, none
of them, to the best of our knowledge, have investigated the impact of outliers
on the reliability and those models' performance. In this study, we investigate
the impact of outliers in a multivariate time series benchmark dataset, namely
SWAN-SF, on flare prediction models, and test our hypothesis. That is, there
exist outliers in SWAN-SF, removal of which enhances the performance of the
prediction models on unseen datasets. We employ Isolation Forest to detect the
outliers among the weaker flare instances. Several experiments are carried out
using a large range of contamination rates which determine the percentage of
present outliers. We asses the quality of each dataset in terms of its actual
contamination using TimeSeriesSVC. In our best finding, we achieve a 279%
increase in True Skill Statistic and 68% increase in Heidke Skill Score. The
results show that overall a significant improvement can be achieved to flare
prediction if outliers are detected and removed properly.
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